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27 - Models for Dyadic Data

from Part VI - Intensive Longitudinal Designs

Published online by Cambridge University Press:  23 March 2020

By
Eduardo Estrada ,
David A. Sbarra  and
Emilio Ferrer
Edited by
Aidan G. C. Wright  and
Michael N. Hallquist
Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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Summary

This chapter revises and describes statistical models for analyzing data from dyadic systems such as therapist-client, mother-children, or romantic partners, among others. It defines interdependence as the key characteristic of dyadic systems, and then identifies clinical research questions related to dyadic systems and processes that unfold over time. These questions are used to select a set of statistical models and data-analytic techniques for answering clinical research questions related to dyadic research. Emphasis is placed on dynamic models that allow transitioning from asking questions about the outcomes (i.e., Did the therapy work?) to questions about the processes and mechanisms (i.e., How did it work?).

Keywords

dyadic datamultivariate data analysisdynamic modelsquantitative psychologylongitudinal data analysishealth psychologystructural equation modelingromantic relationshipstherapist-client relationshipparent-children interaction
Type
Chapter
Information
The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 350 - 368
Publisher: Cambridge University Press
Print publication year: 2020

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References

Anker, M. G., Owen, J., Duncan, B. L., & Sparks, J. A. (2010). The Alliance in Couple Therapy: Partner Influence, Early Change, and Alliance Patterns in a Naturalistic Sample. Journal of Consulting and Clinical Psychology, 78(5), 635–645.CrossRefGoogle Scholar
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1). Retrieved from www.jstatsoft.org/article/view/v067i01Google Scholar
Baucom, B. R., Dickenson, J. A., Atkins, D. C., Baucom, D. H., Fischer, M. S., Weusthoff, S., … Zimmermann, T. (2015a). The Interpersonal Process Model of Demand/Withdraw Behavior. Journal of Family Psychology, 29(1), 80–90.CrossRefGoogle ScholarPubMed
Baucom, B. R., Sheng, E., Christensen, A., Georgiou, P. G., Narayanan, S. S., & Atkins, D. C. (2015b). Behaviorally-Based Couple Therapies Reduce Emotional Arousal during Couple Conflict. Behaviour Research and Therapy, 72, 49–55.Google Scholar
Berghuis, J. P., & Stanton, A. L. (2002). Adjustment to a Dyadic Stressor: A Longitudinal Study of Coping and Depressive Symptoms in Infertile Couples over an Insemination Attempt. Journal of Consulting and Clinical Psychology, 70(2), 433–438.CrossRefGoogle ScholarPubMed
Bisgaard, S., & Kulahci, M. (2011). Time Series Analysis and Forecasting by Example. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Bodenmann, G., Hilpert, P., Nussbeck, F. W., & Bradbury, T. N. (2014). Enhancement of Couples’ Communication and Dyadic Coping by a Self-Directed Approach: A Randomized Controlled Trial. Journal of Consulting and Clinical Psychology, 82(4), 580–591.Google Scholar
Boker, S. M., & Laurenceau, J.-P. (2006). Dynamical Systems Modeling: An Application to the Regulation of Intimacy and Disclosure in Marriage. In Walls, T. A. & Schafer, J. L. (Eds.), Models for Intensive Longitudinal Data (pp. 195–218). New York: Oxford University Press.Google Scholar
Boker, S. M., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., … Bates, T. (2011). OpenMx: An Open Source Extended Structural Equation Modeling Framework. Psychometrika, 76(2), 306–317.Google Scholar
Bolger, N., & Laurenceau, J.-P. (2013). Design and Analysis of Intensive Longitudinal Studies of Distinguishable Dyads. In Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research (pp. 143–175). New York: Guilford Press.Google Scholar
Bollen, K. A., & Curran, P. J. (2006). Latent Curve Models: A Structural Equation Perspective. Hoboken, N.J: Wiley-Interscience.Google Scholar
Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (2017). Changing Dynamics: Time-Varying Autoregressive Models Using Generalized Additive Modeling. Psychological Methods, 22(3), 409–425.Google Scholar
Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., … Kuppens, P. (2016). Assessing Temporal Emotion Dynamics Using Networks. Assessment, 23(4), 425–435.CrossRefGoogle ScholarPubMed
Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., … Tuerlinckx, F. (2013). A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data. PLOS ONE, 8(4), e60188.Google Scholar
Browne, M. W., & Nesselroade, J. R. (2005). Representing Psychological Processes with Dynamic Factor Models: Some Promising Uses and Extensions of Arma Time Series Models. In Maydeu-Olivares, A & McArdle, J. J (Eds.), Advances in Psychometrics: A Festschrift for Roderick P. McDonald (pp. 415–452). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Browne, M. W., & Zhang, G. (2003). DyFA 2.03 User Guide. Retrieved from https://psychology.osu.edu/people/browne.4Google Scholar
Browne, M. W., & Zhang, G. (2007). Developments in the Factor Analysis of Individual Time Series. In Cudeck, R & MacCallum, R. C (Eds.), Factor Analysis at 100: Time Series in Psychology. Historical Developments and Future Directions (pp. 265–291). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Butler, E. A. (2017). Emotions Are Temporal Interpersonal Systems. Current Opinion in Psychology, 17(Supplement C), 129–134.Google Scholar
Butner, J., Diamond, L. M., & Hicks, A. M. (2007). Attachment Style and Two Forms of Affect Coregulation between Romantic Partners. Personal Relationships, 14(3), 431–455.CrossRefGoogle Scholar
Butterfield, R. M., & Lewis, M. A. (2002). Health-Related Social Influence: A Social Ecological Perspective on Tactic Use. Journal of Social and Personal Relationships, 19(4), 505–526.Google Scholar
Cabrieto, J., Tuerlinckx, F., Kuppens, P., Grassmann, M., & Ceulemans, E. (2017). Detecting Correlation Changes in Multivariate Time Series: A Comparison of Four Non-Parametric Change Point Detection Methods. Behavior Research Methods, 49(3), 988–1005.Google Scholar
Campbell, L., Simpson, J. A., Kashy, D. A., & Rholes, W. S. (2001). Attachment Orientations, Dependence, and Behavior in a Stressful Situation: An Application of the Actor-Partner Interdependence Model. Journal of Social and Personal Relationships, 18(6), 821–843.Google Scholar
Castro-Schilo, L., & Ferrer, E. (2013). Comparison of Nomothetic Versus Idiographic-Oriented Methods for Making Predictions about Distal Outcomes from Time Series Data. Multivariate Behavioral Research, 48(2), 175–207.Google Scholar
Chow, S.-M., Ferrer, E., & Hsieh, F. (2011a). Statistical Methods for Modeling Human Dynamics: An Interdisciplinary Dialogue. New York: Taylor & Francis.Google Scholar
Chow, S.-M., Ferrer, E., & Nesselroade, J. R. (2007). An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models. Multivariate Behavioral Research, 42(2), 283–321.Google Scholar
Chow, S.-M., Mattson, W. I., & Messinger, D. S. (2014). Representing Trends and Moment-to-Moment Variability in Dyadic and Family Processes Using State-Space Modeling Techniques. In Emerging Methods in Family Research (pp. 39–55). Cham: Springer.Google Scholar
Chow, S.-M., Nesselroade, J. R., Shifren, K., & McArdle, J. J. (2004). Dynamic Structure of Emotions among Individuals with Parkinson’s Disease. Structural Equation Modeling: A Multidisciplinary Journal, 11(4), 560–582.Google Scholar
Chow, S.-M., Ou, O., Cohn, J. F., & Messinger, D. S. (2017). Representing Self-Organization and Non-Stationarities in Dyadic Interaction Processes Using Dynamic Systems Modeling Techniques. In Von Davier, A., Kyllonen, P. C., & Zhu, M. (Eds.), Innovative Assessment of Collaboration. New York: Springer.Google Scholar
Chow, S.-M., Zu, J., Shifren, K., & Zhang, G. (2011b). Dynamic Factor Analysis Models with Time-Varying Parameters. Multivariate Behavioral Research, 46(2), 303–339.Google Scholar
Cook, W. L., & Snyder, D. K. (2005). Analyzing Nonindependent Outcomes in Couple Therapy Using the Actor-Partner Interdependence Model. Journal of Family Psychology, 19(1), 133–141.Google Scholar
Crowell, S. E., Baucom, B. R., Yaptangco, M., Bride, D., Hsiao, R., McCauley, E., & Beauchaine, T. P. (2014). Emotion Dysregulation and Dyadic Conflict in Depressed and Typical Adolescents: Evaluating Concordance across Psychophysiological and Observational Measures. Biological Psychology, 98(Supplement C), 50–58.Google Scholar
Curran, P. J., & Bollen, K. A. (2001). The Best of Both Worlds: Combining Autoregressive and Latent Curve Models. In Collins, L. M. & Sayer, A. G (Eds.), New Methods for the Analysis of Change (pp. 107–135). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Driscoll, K. A., Schatschneider, C., McGinnity, K., & Modi, A. C. (2012). Application of Dyadic Data Analysis in Pediatric Psychology: Cystic Fibrosis Health-Related Quality of Life and Anxiety in Child-Caregiver Dyads. Journal of Pediatric Psychology, 37(6), 605–611.Google Scholar
Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous Time Structural Equation Modeling with R Package ctsem. Journal of Statistical Software, 77(5). Retrieved from www.jstatsoft.org/article/view/v077i05Google Scholar
Engle, R., & Watson, M. (1981). A One-Factor Multivariate Time Series Model of Metropolitan Wage Rates. Journal of the American Statistical Association, 76(376), 774–781.Google Scholar
Felmlee, D. H. (2006). Application of Dynamic Systems Analysis to Dyadic Interactions. In Ong, D & van Dulmen, M (Eds.), Oxford Handbook of Methods in Positive Psychology (pp. 409–422). New York: Oxford University Press.Google Scholar
Felmlee, D. H., & Greenberg, D. F. (1999). A Dynamic Systems Model of Dyadic Interaction. Journal of Mathematical Sociology, 23(3), 155–180.Google Scholar
Ferrer, E. (2016). Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science. Multivariate Behavioral Research, 51(2–3), 240–256.Google Scholar
Ferrer, E., & Helm, J. L. (2013). Dynamical Systems Modeling of Physiological Coregulation in Dyadic Interactions. International Journal of Psychophysiology, 88(3), 296–308.Google Scholar
Ferrer, E., & McArdle, J. J. (2003). Alternative Structural Models for Multivariate Longitudinal Data Analysis. Structural Equation Modeling: A Multidisciplinary Journal, 10(4), 493–524.Google Scholar
Ferrer, E., & McArdle, J. J. (2004). An Experimental Analysis of Dynamic Hypotheses about Cognitive Abilities and Achievement from Childhood to Early Adulthood. Developmental Psychology, 40(6), 935–952.Google Scholar
Ferrer, E., & McArdle, J. J. (2010). Longitudinal Modeling of Developmental Changes in Psychological Research. Current Directions in Psychological Science, 19(3), 149–154.Google Scholar
Ferrer, E., & Nesselroade, J. R. (2003). Modeling Affective Processes in Dyadic Relations via Dynamic Factor Analysis. Emotion, 3(4), 344–360.Google Scholar
Ferrer, E., & Rast, P. (2017). Partitioning the Variability of Daily Emotion Dynamics in Dyadic Interactions with a Mixed-Effects Location Scale Model. Current Opinion in Behavioral Sciences, 15(Supplement C), 10–15.Google Scholar
Ferrer, E., & Steele, J. (2013). Differential Equations for Evaluating Theoretical Models of Dyadic Interactions. In Molenaar, P. C. M., Lerner, R. M., & Newell, K. M. (Eds.), Handbook of Developmental Systems Theory and Methodology (pp. 345–368). New York: Guilford.Google Scholar
Ferrer, E., & Steele, J. (2014). Differential Equations for Evaluating Theoretical Models of Dyadic Interactions: Handbook of Developmental Systems Theory and Methodology. New York: Guilford.Google Scholar
Ferrer, E., & Steele, J. S. (2012). Dynamic Systems Analysis of Affective Processes in Dyadic Interactions Using Differential Equations. In Hancock, G & Harrings, J (Eds.), Advances in Longitudinal Methods in the Social and Behavioral Sciences (pp. 111–134). Charlotte, NC: Information Age Publishing.Google Scholar
Ferrer, E., & Widaman, K. F. (2008). Dynamic Factor Analysis of Dyadic Affective Processes with Inter-Group Differences. In Card, N. A, Selig, J. P., & Little, T. D. (Eds.), Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences (pp. 107–137). New York: Routledge.Google Scholar
Ferrer, E., & Zhang, G. (2009). Time Series Models for Examining Psychological Processes: Applications and New Developments. In Millsap, R. E. & Maydeu-Olivares, A. (Eds.), Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences (pp. 107–137). London: Sage Publications.Google Scholar
Ferrer, E., Balluerka, N., & Widaman, K. F. (2008). Factorial Invariance and the Specification of Second-Order Latent Growth Models. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 4(1), 22–36.CrossRefGoogle ScholarPubMed
Ferrer, E., Chen, S., Chow, S.-M., & Hsieh, F. (2010). Exploring Intra-Individual, Inter-Individual and Inter-Variable Dynamics in Dyadic Interactions. In Chow, S.-M., Ferrer, E., & Hsieh, F. (Eds.), Statistical Methods for Modeling Human Dynamics: An Interdisciplinary Dialogue (pp. 381–411). New York: Taylor and Francis.Google Scholar
Ferrer, E., McArdle, J. J., Shaywitz, B. A., Holahan, J. M., Marchione, K., & Shaywitz, S. E. (2007). Longitudinal Models of Developmental Dynamics between Reading and Cognition from Childhood to Adolescence. Developmental Psychology, 43(6), 1460–1473.Google Scholar
Ferrer, E., Steele, J. S., & Hsieh, F. (2012). Analyzing the Dynamics of Affective Dyadic Interactions Using Patterns of Intra- and Interindividual Variability. Multivariate Behavioral Research, 47(1), 136–171.Google Scholar
Fisher, A. J., Newman, M. G., & Molenaar, P. C. M. (2011). A Quantitative Method for the Analysis of Nomothetic Relationships between Idiographic Structures: Dynamic Patterns Create Attractor States for Sustained Posttreatment Change. Journal of Consulting and Clinical Psychology, 79(4), 552–563.Google Scholar
Franks, M. M., Wendorf, C. A., Gonzalez, R., & Ketterer, M. (2004). Aid and Influence: Health-Promoting Exchanges of Older Married Partners. Journal of Social and Personal Relationships, 21(4), 431–445.Google Scholar
Garcia-Lopez, L. J., Díaz-Castela, M. del M., Muela-Martinez, J. A., & Espinosa-Fernandez, L. (2014). Can Parent Training for Parents with High Levels of Expressed Emotion Have a Positive Effect on Their Child’s Social Anxiety Improvement? Journal of Anxiety Disorders, 28(8), 812–822.Google Scholar
Girard, J., Wright, A., Beeney, J., Lazarus, S., Scott, L., Stepp, S., & Pilkonis, P. (2017). Interpersonal Problems across Levels of the Psychopathology Hierarchy. Comprehensive Psychiatry, 79, 53–69.Google Scholar
Goldfried, M. R., Greenberg, L. S., & Marmar, C. (1990). Individual Psychotherapy: Process and Outcome. Annual Review of Psychology, 41(1), 659–688.Google Scholar
Gonzalez, R., & Griffin, D. (1997). On the Statistics of Interdependence: Treating Dyadic Data with Respect. In Duck, S. (Ed.), Handbook of Personal Relationships: Theory, Research and Interventions (pp. 271–302). Hoboken, NJ: John Wiley.Google Scholar
Gottman, J. M., & Notarius, C. I. (2000). Decade Review: Observing Marital Interaction. Journal of Marriage and Family, 62(4), 927–947.Google Scholar
Gottman, J. M., Murray, J. D., Swanson, C. C., Tyson, R., & Swanson, K. R. (2002). The Mathematics of Marriage: Dynamic Nonlinear Models. Cambridge, MA: MIT Press.Google Scholar
Gottman, J. M., Swanson, C., & Murray, J. (1999). The Mathematics of Marital Conflict: Dynamic Mathematical Nonlinear Modeling of Newlywed Marital Interaction. Journal of Family Psychology, 13(1), 3–19.Google Scholar
Granic, I., & Patterson, G. R. (2006). Toward a Comprehensive Model of Antisocial Development: A Dynamic Systems Approach. Psychological Review, 113(1), 101–131.CrossRefGoogle Scholar
Hadfield, J. D. (2010). MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package. Journal of Statistical Software, 33(2). Retrieved from www.jstatsoft.org/article/view/v033i02Google Scholar
Hagedoorn, M., Kuijer, R. G., Buunk, B. P., DeJong, G. M., Wobbes, T., & Sanderman, R. (2000). Marital Satisfaction in Patients with Cancer: Does Support from Intimate Partners Benefit Those Who Need It Most? Health Psychology, 19(3), 274–282.Google Scholar
Hamaker, E. L., Zhang, Z., & van der Maas, H. L. J. (2009). Using Threshold Autoregressive Models to Study Dyadic Interactions. Psychometrika, 74(4), 727–745.CrossRefGoogle Scholar
Hawrilenko, M., Gray, T. D., & Córdova, J. V. (2016). The Heart of Change: Acceptance and Intimacy Mediate Treatment Response in a Brief Couples Intervention. Journal of Family Psychology, 30(1), 93–103.CrossRefGoogle Scholar
Hayes, A. M., & Strauss, J. L. (1998). Dynamic Systems Theory as a Paradigm for the Study of Change in Psychotherapy: An Application to Cognitive Therapy for Depression. Journal of Consulting and Clinical Psychology, 66(6), 939–947.CrossRefGoogle Scholar
Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2008). An Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data. Biometrics, 64(2), 627–634.Google Scholar
Helm, J. L., Sbarra, D., & Ferrer, E. (2012). Assessing Cross-Partner Associations in Physiological Responses via Coupled Oscillator Models. Emotion, 12(4), 748–762.Google Scholar
Helm, J. L., Sbarra, D. A., & Ferrer, E. (2014). Coregulation of Respiratory Sinus Arrhythmia in Adult Romantic Partners. Emotion, 14(3), 522–531.Google Scholar
Hollon, S. D., Muñoz, R. F., Barlow, D. H., Beardslee, W. R., Bell, C. C., Bernal, G., … Sommers, D. (2002). Psychosocial Intervention Development for the Prevention and Treatment of Depression: Promoting Innovation and Increasing Access. Biological Psychiatry, 52(6), 610–630.Google Scholar
Hsieh, F., Ferrer, E., Chen, S.-C., & Chow, S.-M. (2010). Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation. Psychometrika, 75(2), 351–372.CrossRefGoogle Scholar
Kenny, D. A. (1996). Models of Non-Independence in Dyadic Research. Journal of Social and Personal Relationships, 13(2), 279–294.Google Scholar
Kenny, D. A., & Judd, C. M. (1986). Consequences of Violating the Independence Assumption in Analysis of Variance. Psychological Bulletin, 99(3), 422–431.Google Scholar
Kenny, D. A., Kashy, D. A., & Bolger, N. (1998). Data Analysis in Social Psychology. In Gilbert, D., Fiske, S., & Lindzey, G. (Eds.), Handbook of Social Psychology (4th edn., Vol. 1, pp. 233–265). Boston: McGraw-Hill.Google Scholar
Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic Data Analysis. New York: Guilford Press.Google Scholar
Kivlighan, D. M., Clements, L., Blake, C., Arnzen, A., & Brady, L. (1993). Counselor Sex Role Orientation, Flexibility, and Working Alliance Formation. Journal of Counseling & Development, 72(1), 95–100.Google Scholar
Kivlighan, D. M., Marmarosh, C. L., & Hilsenroth, M. J. (2014). Client and Therapist Therapeutic Alliance, Session Evaluation, and Client Reliable Change: A Moderated Actor-Partner Interdependence Model. Journal of Counseling Psychology, 61(1), 15–23.Google Scholar
Kline, R. B. (2016). Computer Tools. In Principles and Practice of Structural Equation Modeling (4th edn., pp. 97–113). New York: Guilford Press.Google Scholar
Kopta, S. M., Lueger, R. J., Saunders, S. M., & Howard, K. I. (1999). Individual Psychotherapy Outcome and Process Research: Challenges Leading to Greater Turmoil or a Positive Transition? Annual Review of Psychology, 50(1), 441–469.Google Scholar
Kouros, C. D., & Cummings, E. M. (2010). Longitudinal Associations Between Husbands’ and Wives’ Depressive Symptoms. Journal of Marriage and Family, 72(1), 135–147.Google Scholar
Laurenceau, J.-P., & Bolger, N. (2005). Using Diary Methods to Study Marital and Family Processes. Journal of Family Psychology, 19(1), 86–97.Google Scholar
Laurenceau, J.-P., Hayes, A. M., & Feldman, G. C. (2007). Some Methodological and Statistical Issues in the Study of Change Processes in Psychotherapy. Clinical Psychology Review, 27(6), 682–695.Google Scholar
Lawrence, E., Yoon, J., Langer, A., & Ro, E. (2009). Is Psychological Aggression as Detrimental as Physical Aggression? The Independent Effects of Psychological Aggression on Depression and Anxiety Symptoms. Violence and Victims, 24(1), 20–35.Google Scholar
Ledermann, T., & Kenny, D. A. (2012). The Common Fate Model for Dyadic Data: Variations of a Theoretically Important but Underutilized Model. Journal of Family Psychology, 26(1), 140–148.Google Scholar
Ledermann, T., & Macho, S. (2014). Analyzing Change at the Dyadic Level: The Common Fate Growth Model. Journal of Family Psychology, 28(2), 204–213.Google Scholar
Ledermann, T., Macho, S., & Kenny, D. A. (2011). Assessing Mediation in Dyadic Data Using the Actor-Partner Interdependence Model. Structural Equation Modeling: A Multidisciplinary Journal, 18(4), 595–612.Google Scholar
Levenson, R. W., & Gottman, J. M. (1983). Marital Interaction: Physiological Linkage and Affective Exchange. Journal of Personality and Social Psychology, 45(3), 587–597.Google Scholar
Madhyastha, T. M., Hamaker, E. L., & Gottman, J. M. (2011). Investigating Spousal Influence Using Moment-to-Moment Affect Data from Marital Conflict. Journal of Family Psychology, 25(2), 292–300.Google Scholar
Marmarosh, C. L., Kivlighan, D. M., Bieri, K., LaFauci Schutt, J. M., Barone, C., & Choi, J. (2014). The Insecure Psychotherapy Base: Using Client and Therapist Attachment Styles to Understand the Early Alliance. Psychotherapy, 51(3), 404–412.Google Scholar
McArdle, J. J. (1982). Structural Equation Modeling of an Individual System: Preliminary Results from “A Case Study in Episodic Alcoholism.” Unpublished Manuscript, Department of Psychology, University of Denver.Google Scholar
McArdle, J. J. (1988). Dynamic but Structural Equation Modeling of Repeated Measures Data. In Nesselroade, J. R. & Cattell, R. B. (Eds.), Handbook of Multivariate Experimental Psychology (pp. 561–614). Boston, MA: Springer.CrossRefGoogle Scholar
McArdle, J. J. (2001). A Latent Difference Score Approach to Longitudinal Dynamic Structural Analysis. In Cudeck, R., du Toit, S., & Sörbom, D. (Eds.), Structural Equation Modeling, Present and Future: A Festschrift in Honor of Karl Jöreskog (pp. 7–46). Lincolnwood, IL: Scientific Software International.Google Scholar
McArdle, J. J. (2009). Latent Variable Modeling of Differences and Changes with Longitudinal Data. Annual Review of Psychology, 60(1), 577–605.Google Scholar
McArdle, J. J., & Epstein, D. (1987). Latent Growth Curves within Developmental Structural Equation Models. Child Development, 58(1), 110–133.Google Scholar
McArdle, J. J., & Hamagami, F. (2001). Latent Difference Score Structural Models for Linear Dynamic Analyses with Incomplete Longitudinal Data. In Collins, L. M. & Sayer, A. G. (Eds.), New Methods for the Analysis of Change (pp. 139–175). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
McArdle, J. J., Hamagami, F., Meredith, W., & Bradway, K. P. (2000). Modeling the Dynamic Hypotheses of Gf–Gc Theory Using Longitudinal Life-Span Data. Learning and Individual Differences, 12(1), 53–79.Google Scholar
Medina-Pradas, C., Navarro, J. B., López, S. R., Grau, A., & Obiols, J. E. (2011). Dyadic View of Expressed Emotion, Stress, and Eating Disorder Psychopathology. Appetite, 57(3), 743–748.Google Scholar
Meredith, W. (1993). Measurement Invariance, Factor Analysis And Factorial Invariance. Psychometrika, 58(4), 525–543.Google Scholar
Meredith, W., & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122.Google Scholar
Molenaar, P. C. M. (1985). A Dynamic Factor Model for the Analysis of Multivariate Time Series. Psychometrika, 50(2), 181–202.Google Scholar
Molenaar, P. C. M., & Nesselroade, J. R. (2001). Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series. Psychometrika, 66(1), 99–107.Google Scholar
Molenaar, P. C. M., Gooijer, J. G. D., & Schmitz, B. (1992). Dynamic Factor Analysis of Nonstationary Multivariate Time Series. Psychometrika, 57(3), 333–349.Google Scholar
Muthén, L. K., & Muthén, B. O. (1998). Mplus User’s Guide (6th edn.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., … Boker, S. M. (2016). OpenMx 2.0: Extended Structural Equation and Statistical Modeling. Psychometrika, 81(2), 535–549.Google Scholar
Nesselroade, J. R., & Boker, S. M. (1994). Assessing Constancy and Change. In Heatherton, T. & Weinberg, J. (Eds.), Can Personality Change? (pp. 121–147). Washington, DC: American Psychological Association.Google Scholar
Nestler, S., Grimm, K. J., & Schönbrodt, F. D. (2015). The Social Consequences and Mechanisms of Personality: How to Analyse Longitudinal Data from Individual, Dyadic, Round-Robin and Network Designs. European Journal of Personality, 29(2), 272–295.Google Scholar
Ou, L., Hunter, M. D., & Chow, S.-M. (2017). What’s for dynr. A Package for Linear and Nonlinear DYNamic Modeling in R. Retrieved from https://quantdev.ssri.psu.edu/sites/qdev/files/OuHunterChow_Dynr.pdfGoogle Scholar
Perry, N. S., Baucom, K. J. W., Bourne, S., Butner, J., Crenshaw, A. O., Hogan, J. N., … Baucom, B. R. W. (2017). Graphic Methods for Interpreting Longitudinal Dyadic Patterns from Repeated-Measures Actor-Partner Interdependence models. Journal of Family Psychology, 31(5), 592–603.Google Scholar
Proulx, C. M., & Snyder-Rivas, L. A. (2013). The Longitudinal Associations between Marital Happiness, Problems, and Self-Rated Health. Journal of Family Psychology, 27(2), 194–202.Google Scholar
Przeworski, A., Zoellner, L. A., Franklin, M. E., Garcia, A., Freeman, J., March, J. S., & Foa, E. B. (2012). Maternal and Child Expressed Emotion as Predictors of Treatment Response in Pediatric Obsessive-Compulsive Disorder. Child Psychiatry & Human Development, 43(3), 337–353.Google Scholar
Core Team, R. (2017). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.Google Scholar
Ram, N., Shiyko, M., Lunkenheimer, E. S., Doerksen, S., & Conroy, D. (2014). Families as Coordinated Symbiotic Systems: Making Use of Nonlinear Dynamic Models. In Emerging Methods in Family Research (pp. 19–37). Cham: Springer.Google Scholar
Rao, C. R. (1958). Some Statistical Methods for Comparison of Growth Curves. Biometrics, 14(1), 1–17.Google Scholar
Rast, P., Hofer, S. M., & Sparks, C. (2012). Modeling Individual Differences in Within-Person Variation of Negative and Positive Affect in a Mixed Effects Location Scale Model Using BUGS/JAGS. Multivariate Behavioral Research, 47(2), 177–200.Google Scholar
Reed, R. G., Barnard, K., & Butler, E. A. (2015). Distinguishing Emotional Coregulation from Codysregulation: An Investigation of Emotional Dynamics and Body Weight in Romantic Couples. Emotion, 15(1), 45–60.Google Scholar
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2). Retrieved from www.jstatsoft.org/article/view/v048i02Google Scholar
Sbarra, D. A., & Ferrer, E. (2006). The Structure and Process of Emotional Experience following Nonmarital Relationship Dissolution: Dynamic Factor Analyses of Love, Anger, and Sadness. Emotion, 6(2), 224–238.Google Scholar
Sbarra, D. A., & Wishman, M. A. (2013). Marital and Relational Discord. In Castonguay, L. & Oltmans, T. C. (Eds.), Psychopathology: Bridging the Gap between Basic Empirical Findings and Clinical Practice (pp. 393–418). New York: Guilford Press.Google Scholar
Shifren, K., Hooker, K., Wood, P., & Nesselroade, J. R. (1997). Structure and Variation of Mood in Individuals with Parkinson’s Disease: A Dynamic Factor Analysis. Psychology and Aging, 12(2), 328–339.CrossRefGoogle ScholarPubMed
Shumway, R. H., & Stoffer, D. S. (2011). Time Series Analysis and Its Applications: With R Examples (3rd edn.). New York: Springer.Google Scholar
Steele, J. S., & Ferrer, E. (2011). Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes. Multivariate Behavioral Research, 46(6), 956–984.Google Scholar
Steele, J. S., Gonzales, J. E., & Ferrer, E. (2018). Uses and Limitations of Continuous Time Models to Examine Dyadic Interactions. In van Montfort, K., Oud, J., & Voelkle, M. C. (Eds.), Continuous Time Modeling in the Behavioral and Related Sciences (pp. 135–162). Cham: Springer International.Google Scholar
Thompson, A., & Bolger, N. (1999). Emotional Transmission in Couples under Stress. Journal of Marriage and the Family, 61(1), 38–48.Google Scholar
Thorson, K., West, T., & Mendes, W. (2017). Measuring Physiological Influence in Dyads: A Guide to Designing, Implementing, and Analyzing Dyadic Physiological Studies. PsyArXiv. Retrieved from https://doi.org/10.17605/OSF.IO/9NDKFGoogle Scholar
Zhang, G., & Browne, M. W. (2008). DyFA Bootstrap: Dynamic Factor Analysis of Lagged Correlation Matrices with Bootstrap Standard Errors and Goodness of Fit Test. Version: Beta 1. Retrieved from https://psychology.osu.edu/people/browne.4Google Scholar

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